Triple
T16277992
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ellesmere Port railway station |
E395184
|
entity |
| Predicate | stationCode |
P1289
|
FINISHED |
| Object |
ELP
ELP is the three-letter National Rail station code for Ellesmere Port railway station in Cheshire, England.
|
E1206047
|
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: ELP | Statement: [Ellesmere Port railway station, stationCode, ELP]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: ELP Context triple: [Ellesmere Port railway station, stationCode, ELP]
-
A.
ELP
ELP is the three-letter IATA airport code for El Paso International Airport, a commercial airport serving El Paso, Texas.
-
B.
ELP
ELP is a British progressive rock supergroup best known for its virtuosic musicianship, elaborate live performances, and influential 1970s albums.
-
C.
ELPA
ELPA (Emacs Lisp Package Archive) is a primary repository and distribution system for Emacs Lisp packages used to extend and customize the Emacs editor.
-
D.
ELC
ELC is the regional vehicle registration code assigned to the area that includes the village of Walewice in Poland.
-
E.
ELM
ELM is the commonly used abbreviation for the Estonian Literary Museum, a national research and memory institution dedicated to preserving and studying Estonia’s literary and folkloric heritage.
- 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: ELP Triple: [Ellesmere Port railway station, stationCode, ELP]
Generated description
ELP is the three-letter National Rail station code for Ellesmere Port railway station in Cheshire, England.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: ELP Target entity description: ELP is the three-letter National Rail station code for Ellesmere Port railway station in Cheshire, England.
-
A.
ELP
ELP is the three-letter IATA airport code for El Paso International Airport, a commercial airport serving El Paso, Texas.
-
B.
ELP
ELP is a British progressive rock supergroup best known for its virtuosic musicianship, elaborate live performances, and influential 1970s albums.
-
C.
ELPA
ELPA (Emacs Lisp Package Archive) is a primary repository and distribution system for Emacs Lisp packages used to extend and customize the Emacs editor.
-
D.
ELC
ELC is the regional vehicle registration code assigned to the area that includes the village of Walewice in Poland.
-
E.
ELM
ELM is the commonly used abbreviation for the Estonian Literary Museum, a national research and memory institution dedicated to preserving and studying Estonia’s literary and folkloric heritage.
- 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_69d87f22c7248190a54c949738441e2e |
completed | April 10, 2026, 4:40 a.m. |
| NER | Named-entity recognition | batch_69e2460f73648190b5c931f2ba2a09da |
completed | April 17, 2026, 2:39 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_6a001f93240881909d0beaddc92f0ad5 |
completed | May 10, 2026, 6:02 a.m. |
| NEDg | Description generation | batch_6a0021459c4081908e4c1d2e0bc8a5be |
completed | May 10, 2026, 6:10 a.m. |
| NED2 | Entity disambiguation (via description) | batch_6a002221fe7c819083c8ede5e63b0908 |
completed | May 10, 2026, 6:13 a.m. |
Created at: April 10, 2026, 5:05 a.m.