Triple
T11737448
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Michel Serres |
E279066
|
entity |
| Predicate | familyName |
P18
|
FINISHED |
| Object |
Serres
Serres is a French surname most notably associated with the philosopher and historian of science Michel Serres.
|
E944372
|
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: Serres | Statement: [Michel Serres, familyName, Serres]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Serres Context triple: [Michel Serres, familyName, Serres]
-
A.
Serres
Serres is a historic city in northern Greece known for its Byzantine heritage and role as a regional economic and cultural center.
-
B.
San Javier
San Javier is a Chilean town known for its agricultural activity and wine production in the Maule Region.
-
C.
San Javier
San Javier is a municipality in Spain’s Region of Murcia, known for hosting the Spanish Air and Space Force’s main officer training academy and its nearby coastal and lagoon areas on the Mar Menor.
-
D.
San Javier
San Javier is a town in the Mexican state of Baja California Sur, known for its historic mission and role as a regional cultural and religious center.
-
E.
Lavezares
Lavezares is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and island landscapes.
- 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: Serres Triple: [Michel Serres, familyName, Serres]
Generated description
Serres is a French surname most notably associated with the philosopher and historian of science Michel Serres.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Serres Target entity description: Serres is a French surname most notably associated with the philosopher and historian of science Michel Serres.
-
A.
Serres
Serres is a historic city in northern Greece known for its Byzantine heritage and role as a regional economic and cultural center.
-
B.
San Javier
San Javier is a Chilean town known for its agricultural activity and wine production in the Maule Region.
-
C.
San Javier
San Javier is a municipality in Spain’s Region of Murcia, known for hosting the Spanish Air and Space Force’s main officer training academy and its nearby coastal and lagoon areas on the Mar Menor.
-
D.
San Javier
San Javier is a town in the Mexican state of Baja California Sur, known for its historic mission and role as a regional cultural and religious center.
-
E.
Lavezares
Lavezares is a coastal municipality in the province of Northern Samar in the Philippines, known for its fishing communities and island landscapes.
- 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_69d6aaffec6881908bead509e8621742 |
completed | April 8, 2026, 7:22 p.m. |
| NER | Named-entity recognition | batch_69d8a4ef1c4881909ad36dc27b1fe193 |
completed | April 10, 2026, 7:21 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f019b318188190bfb7effcf42974d2 |
completed | April 28, 2026, 2:21 a.m. |
| NEDg | Description generation | batch_69f0319520dc8190817c5e75ddb7d40b |
completed | April 28, 2026, 4:03 a.m. |
| NED2 | Entity disambiguation (via description) | batch_69f05aa351888190a31092e6a9aee26b |
completed | April 28, 2026, 6:58 a.m. |
Created at: April 8, 2026, 9:41 p.m.