Triple
T5582743
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Pinner |
E146677
|
entity |
| Predicate | postTown |
P2711
|
FINISHED |
| Object |
PINNER
PINNER is a suburban area in the London Borough of Harrow, known for its residential character and village-like high street.
|
E529407
|
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: PINNER | Statement: [Pinner, postTown, PINNER]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: PINNER Context triple: [Pinner, postTown, PINNER]
-
A.
Personal Shopper
Personal Shopper is a 2016 psychological thriller film directed by Olivier Assayas, in which Kristen Stewart plays a grieving medium working in Paris while confronting both supernatural and personal hauntings.
-
B.
Pashons
Pashons is the ninth month of the Coptic calendar, traditionally associated with the harvest season in Egypt.
-
C.
Pijin
Pijin is an English-based creole language widely used as a lingua franca in the Solomon Islands.
-
D.
Stitch Fix
Stitch Fix is an online personal styling service and apparel retailer that uses data science and human stylists to curate and ship customized clothing selections to customers.
-
E.
Pitti
Pitti is a small, uninhabited coral islet in the Lakshadweep archipelago of India, known as an important nesting site for seabirds.
- 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: PINNER Triple: [Pinner, postTown, PINNER]
Generated description
PINNER is a suburban area in the London Borough of Harrow, known for its residential character and village-like high street.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: PINNER Target entity description: PINNER is a suburban area in the London Borough of Harrow, known for its residential character and village-like high street.
-
A.
Personal Shopper
Personal Shopper is a 2016 psychological thriller film directed by Olivier Assayas, in which Kristen Stewart plays a grieving medium working in Paris while confronting both supernatural and personal hauntings.
-
B.
Pashons
Pashons is the ninth month of the Coptic calendar, traditionally associated with the harvest season in Egypt.
-
C.
Pijin
Pijin is an English-based creole language widely used as a lingua franca in the Solomon Islands.
-
D.
Stitch Fix
Stitch Fix is an online personal styling service and apparel retailer that uses data science and human stylists to curate and ship customized clothing selections to customers.
-
E.
Pitti
Pitti is a small, uninhabited coral islet in the Lakshadweep archipelago of India, known as an important nesting site for seabirds.
- 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_69c0090287a08190b4098411effe970c |
completed | March 22, 2026, 3:21 p.m. |
| NER | Named-entity recognition | batch_69c0208333f08190bf0049b6bdd280f5 |
completed | March 22, 2026, 5:01 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69c0285e7bc08190bd5a08c50679e9d9 |
completed | March 22, 2026, 5:35 p.m. |
| NEDg | Description generation | batch_69c037fca93881908d4d7403bfb1f866 |
completed | March 22, 2026, 6:42 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69c03898327c8190bd3b889bd7663003 |
completed | March 22, 2026, 6:44 p.m. |
Created at: March 22, 2026, 3:37 p.m.