Triple
T1654258
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Xavi Hernández |
E35761
|
entity |
| Predicate | placeOfBirth |
P1
|
FINISHED |
| Object |
Terrassa
Terrassa is a city in Catalonia, Spain, known as part of the Barcelona metropolitan area and for its industrial heritage and modernist architecture.
|
E188972
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Terrassa | Statement: [Xavi Hernández, placeOfBirth, Terrassa]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Terrassa Context triple: [Xavi Hernández, placeOfBirth, Terrassa]
-
A.
Valldemossa
Valldemossa is a picturesque mountain village on the Spanish island of Mallorca, renowned for its historic Carthusian monastery and scenic stone streets.
-
B.
Manresa
Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
-
C.
Es Trenc
Es Trenc is a famous natural beach on the southern coast of Mallorca, known for its long stretch of white sand and clear turquoise waters.
-
D.
Santpedor
Santpedor is a small town in Catalonia, Spain, best known internationally as the birthplace of football manager Pep Guardiola.
-
E.
Ayguemarse
Ayguemarse is a smaller watercourse in southeastern France that serves as one of the contributing streams feeding the Ouvèze River.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Terrassa Triple: [Xavi Hernández, placeOfBirth, Terrassa]
Generated description
Terrassa is a city in Catalonia, Spain, known as part of the Barcelona metropolitan area and for its industrial heritage and modernist architecture.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Terrassa Target entity description: Terrassa is a city in Catalonia, Spain, known as part of the Barcelona metropolitan area and for its industrial heritage and modernist architecture.
-
A.
Valldemossa
Valldemossa is a picturesque mountain village on the Spanish island of Mallorca, renowned for its historic Carthusian monastery and scenic stone streets.
-
B.
Manresa
Manresa is a historic city in Catalonia, Spain, known for its medieval architecture and significance as a religious and commercial center in the region.
-
C.
Es Trenc
Es Trenc is a famous natural beach on the southern coast of Mallorca, known for its long stretch of white sand and clear turquoise waters.
-
D.
Santpedor
Santpedor is a small town in Catalonia, Spain, best known internationally as the birthplace of football manager Pep Guardiola.
-
E.
Ayguemarse
Ayguemarse is a smaller watercourse in southeastern France that serves as one of the contributing streams feeding the Ouvèze River.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a8860568888190a32cd9f70acbba42 |
completed | March 4, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69a90a8a080c8190b6913d6830d74526 |
completed | March 5, 2026, 4:46 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ad71a8c22c8190b7f2883dfbd1403f |
completed | March 8, 2026, 12:55 p.m. |
| NEDg | Description generation | batch_69ad72371b648190a50b5b5ca9cd7f5d |
completed | March 8, 2026, 12:57 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ad72a64e0c8190a2b63c78c54896d0 |
completed | March 8, 2026, 12:59 p.m. |
Created at: March 4, 2026, 7:29 p.m.